A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers' Using Teacher's

Author(s)

Barreto, Candida ^[1] ; Bruneri, Guilherme de Albuquerque ^[2] ; Brockington, Guilherme ^[3] ; Ayaz, Hasan ^{[4, 5, 6, 7, 8]} ; Sato, Joao Ricardo ^{[1, 9]}

Affiliation

^[1] Fed Univ ABC, Ctr Math Comp & Cognit, Sao Bernardo Do Campo - Brazil

^[2] Univ Fed Sao Paulo, Phys Inst, Sao Paulo - Brazil

^[3] Univ Fed ABC, Ctr Nat & Human Sci, Santo Andre, SP - Brazil

^[4] Drexel Univ, Sch Biomed Engn, Philadelphia, PA 19104 - USA

^[5] Drexel Univ, Coll Arts & Sci, Dept Psychol, Philadelphia, PA 19104 - USA

^[6] Drexel Univ, Drexel Solut Inst, Philadelphia, PA 19104 - USA

^[7] Univ Penn, Dept Family & Community Hlth, Philadelphia, PA 19104 - USA

^[8] Childrens Hosp Philadelphia, Ctr Injury Res & Prevent, Philadelphia, PA 19104 - USA

^[9] Fed Univ ABC, Interdisciplinary Unit Appl Neurosci, Sao Bernardo Do Campo - Brazil

• Document type: Journal article
• Source: FRONTIERS IN HUMAN NEUROSCIENCE; v. 15, MAY 7 2021.
• Web of Science Citations: 0

Abstract

Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support vector regression (SVR) to predict one brain activity time series using another as the predictor. We applied the proposed methodology to explore the teacher-student interaction, which plays a critical role in the formal learning process. In an illustrative application, we collected fNIRS data of the teacher and preschoolers' dyads performing an interaction task. The teacher explained to the child how to add two numbers in the context of a game. The Prefrontal cortex and temporal-parietal junction of both teacher and student were recorded. A multivariate regression model was built for each channel in each dyad, with the student's signal as the response variable and the teacher's ones as the predictors. We compared the predictions of SVR with the conventional ordinary least square (OLS) predictor. The results predicted by the SVR model were statistically significantly correlated with the actual test data at least one channel-pair for all dyads. Overall, 29/90 channel-pairs across the five dyads (18 channels 5 dyads = 90 channel-pairs) presented significant signal predictions withthe SVR approach. The conventional OLS resulted in only 4 out of 90 valid predictions. These results demonstrated that the SVR could be used to perform channel-wise predictions across individuals, and the teachers' cortical activity can be used to predict the student brain hemodynamic response. (AU)

• FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
• Grantee: André Fujita
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/04654-9 - Time series, wavelets and high dimensional data
• Grantee: Pedro Alberto Morettin
• Support Opportunities: Research Projects - Thematic Grants